{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:GGL3YVZ3KSXCKYABUZIXFNRFEK","short_pith_number":"pith:GGL3YVZ3","schema_version":"1.0","canonical_sha256":"3197bc573b54ae256001a65172b62522aa5bab8ab80f6435a894c3ecb5b4fa5f","source":{"kind":"arxiv","id":"1705.09415","version":2},"attestation_state":"computed","paper":{"title":"Near-Optimal Belief Space Planning via T-LQG","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"cs.RO","authors_text":"Mohammadhussein Rafieisakhaei, P. R. Kumar, Suman Chakravorty","submitted_at":"2017-05-26T02:31:44Z","abstract_excerpt":"We consider the problem of planning under observation and motion uncertainty for nonlinear robotics systems. Determining the optimal solution to this problem, generally formulated as a Partially Observed Markov Decision Process (POMDP), is computationally intractable. We propose a Trajectory-optimized Linear Quadratic Gaussian (T-LQG) approach that leads to quantifiably near-optimal solutions for the POMDP problem. We provide a novel \"separation principle\" for the design of an optimal nominal open-loop trajectory followed by an optimal feedback control law, which provides a near-optimal feedba"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1705.09415","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-05-26T02:31:44Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"095c5b815c459bd574907c0687328130396d3d502a35734116d92d2776ee12db","abstract_canon_sha256":"a7477b053ad9931580571d39553aaae0c2d6065663be6fb7553486ef3b194c5e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:39.956259Z","signature_b64":"dwvEGJr95Drf7oCv3vLw4mYzv/oXLFxxchsuNXFOwOdgEZaREG//HfRjgoQ4wcIvhxQRoaPkxZtpvhRIL7CxAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3197bc573b54ae256001a65172b62522aa5bab8ab80f6435a894c3ecb5b4fa5f","last_reissued_at":"2026-05-18T00:40:39.955533Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:39.955533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Near-Optimal Belief Space Planning via T-LQG","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"cs.RO","authors_text":"Mohammadhussein Rafieisakhaei, P. R. Kumar, Suman Chakravorty","submitted_at":"2017-05-26T02:31:44Z","abstract_excerpt":"We consider the problem of planning under observation and motion uncertainty for nonlinear robotics systems. Determining the optimal solution to this problem, generally formulated as a Partially Observed Markov Decision Process (POMDP), is computationally intractable. We propose a Trajectory-optimized Linear Quadratic Gaussian (T-LQG) approach that leads to quantifiably near-optimal solutions for the POMDP problem. We provide a novel \"separation principle\" for the design of an optimal nominal open-loop trajectory followed by an optimal feedback control law, which provides a near-optimal feedba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.09415","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1705.09415","created_at":"2026-05-18T00:40:39.955655+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.09415v2","created_at":"2026-05-18T00:40:39.955655+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.09415","created_at":"2026-05-18T00:40:39.955655+00:00"},{"alias_kind":"pith_short_12","alias_value":"GGL3YVZ3KSXC","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"GGL3YVZ3KSXCKYAB","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"GGL3YVZ3","created_at":"2026-05-18T12:31:15.632608+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK","json":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK.json","graph_json":"https://pith.science/api/pith-number/GGL3YVZ3KSXCKYABUZIXFNRFEK/graph.json","events_json":"https://pith.science/api/pith-number/GGL3YVZ3KSXCKYABUZIXFNRFEK/events.json","paper":"https://pith.science/paper/GGL3YVZ3"},"agent_actions":{"view_html":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK","download_json":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK.json","view_paper":"https://pith.science/paper/GGL3YVZ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.09415&json=true","fetch_graph":"https://pith.science/api/pith-number/GGL3YVZ3KSXCKYABUZIXFNRFEK/graph.json","fetch_events":"https://pith.science/api/pith-number/GGL3YVZ3KSXCKYABUZIXFNRFEK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK/action/storage_attestation","attest_author":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK/action/author_attestation","sign_citation":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK/action/citation_signature","submit_replication":"https://pith.science/pith/GGL3YVZ3KSXCKYABUZIXFNRFEK/action/replication_record"}},"created_at":"2026-05-18T00:40:39.955655+00:00","updated_at":"2026-05-18T00:40:39.955655+00:00"}